import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class   _UpperCamelCase (		_UpperCAmelCase		,_UpperCAmelCase					):


			"""simple docstring"""






			@register_to_config
			def __init__(  self ,  *,
			    lowerCAmelCase__ = 4 ,  lowerCAmelCase__ = 7_68 ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  )	->   Dict:

							'''simple docstring'''


							super().__init__()

							__lowercase					     =				nn.Parameter(torch.zeros(lowerCAmelCase__		)		)

							# parameters for additional clip time embeddings
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)

							# parameters for encoder hidden states
							__lowercase					     =				clip_extra_context_tokens
							__lowercase					     =				nn.Linear(
							    lowerCAmelCase__ ,  self.clip_extra_context_tokens * cross_attention_dim		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.LayerNorm(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  *, lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   List[str]:

							'''simple docstring'''


							if do_classifier_free_guidance:
											# Add the classifier free guidance embeddings to the image embeddings
											__lowercase					     =				image_embeddings.shape[0]
											__lowercase					     =				self.learned_classifier_free_guidance_embeddings.unsqueeze(0		)
											__lowercase					     =				classifier_free_guidance_embeddings.expand(
											    lowerCAmelCase__ ,  -1		)
											__lowercase					     =				torch.cat([classifier_free_guidance_embeddings, image_embeddings] ,  dim=0		)

							# The image embeddings batch size and the text embeddings batch size are equal
							assert image_embeddings.shape[0] == prompt_embeds.shape[0]

							__lowercase					     =				prompt_embeds.shape[0]

							# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
							# adding CLIP embeddings to the existing timestep embedding, ...
							__lowercase					     =				self.embedding_proj(lowerCAmelCase__		)
							__lowercase					     =				self.clip_image_embeddings_project_to_time_embeddings(lowerCAmelCase__		)
							__lowercase					     =				time_projected_image_embeddings + time_projected_prompt_embeds

							# ... and by projecting CLIP embeddings into four
							# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
							__lowercase					     =				self.clip_extra_context_tokens_proj(lowerCAmelCase__		)
							__lowercase					     =				clip_extra_context_tokens.reshape(lowerCAmelCase__ ,  -1 ,  self.clip_extra_context_tokens		)
							__lowercase					     =				clip_extra_context_tokens.permute(0 ,  2 ,  1		)

							__lowercase					     =				self.encoder_hidden_states_proj(lowerCAmelCase__		)
							__lowercase					     =				self.text_encoder_hidden_states_norm(lowerCAmelCase__		)
							__lowercase					     =				torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] ,  dim=1		)

							return text_encoder_hidden_states, additive_clip_time_embeddings
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__a    :	Optional[Any]         =    logging.get_logger(__name__)

__a    :	Optional[Any]         =    torch.device("""cpu""")





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__lowercase					     =				Image.open(requests.get(lowercase				,     stream=lowercase				).raw				)
				return im





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				if swiftformer_name == "swiftformer_xs":
								return torch.tensor([-2.1703E00, 2.1107E00, -2.0811E00, 8.8685E-01, 2.4360E-01]				)

				elif swiftformer_name == "swiftformer_s":
								return torch.tensor([3.9636E-01, 2.3478E-01, -1.6963E00, -1.7381E00, -8.6337E-01]				)

				elif swiftformer_name == "swiftformer_l1":
								return torch.tensor([-4.2768E-01, -4.7429E-01, -1.0897E00, -1.0248E00, 3.5523E-02]				)

				elif swiftformer_name == "swiftformer_l3":
								return torch.tensor([-2.5330E-01, 2.4211E-01, -6.0185E-01, -8.2789E-01, -6.0446E-02]				)





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				dct.pop(lowercase				)
				__lowercase					     =				val





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				[]
				for k in state_dict.keys():
								__lowercase					     =				k
								if ".pwconv" in k:
												__lowercase					     =				k_new.replace('''.pwconv'''				,     '''.point_wise_conv'''				)
								if ".dwconv" in k:
												__lowercase					     =				k_new.replace('''.dwconv'''				,     '''.depth_wise_conv'''				)
								if ".Proj." in k:
												__lowercase					     =				k_new.replace('''.Proj.'''				,     '''.proj.'''				)
								if "patch_embed" in k_new:
												__lowercase					     =				k_new.replace('''patch_embed'''				,     '''swiftformer.patch_embed.patch_embedding'''				)
								if "network" in k_new:
												__lowercase					     =				k_new.split('''.'''				)
												if ls[2].isdigit():
																__lowercase					     =				'''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:]				)
												else:
																__lowercase					     =				k_new.replace('''network'''				,     '''swiftformer.encoder.network'''				)
								rename_keys.append((k, k_new)				)
				return rename_keys





@torch.no_grad()
def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				SwiftFormerConfig()

				# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
				__lowercase					     =				1000
				__lowercase					     =				'''huggingface/label-files'''
				__lowercase					     =				'''imagenet-1k-id2label.json'''
				__lowercase					     =				json.load(open(hf_hub_download(lowercase				,     lowercase				,     repo_type='''dataset'''				)				,     '''r'''				)				)
				__lowercase					     =				{int(lowercase				): v for k, v in idalabel.items()}
				__lowercase					     =				idalabel
				__lowercase					     =				{v: k for k, v in idalabel.items()}

				# size of the architecture
				if swiftformer_name == "swiftformer_xs":
								__lowercase					     =				[3, 3, 6, 4]
								__lowercase					     =				[48, 56, 112, 220]

				elif swiftformer_name == "swiftformer_s":
								__lowercase					     =				[3, 3, 9, 6]
								__lowercase					     =				[48, 64, 168, 224]

				elif swiftformer_name == "swiftformer_l1":
								__lowercase					     =				[4, 3, 10, 5]
								__lowercase					     =				[48, 96, 192, 384]

				elif swiftformer_name == "swiftformer_l3":
								__lowercase					     =				[4, 4, 12, 6]
								__lowercase					     =				[64, 128, 320, 512]

				# load state_dict of original model, remove and rename some keys
				if original_ckpt:
								if original_ckpt.startswith('''https'''				):
												__lowercase					     =				torch.hub.load_state_dict_from_url(lowercase				,     map_location='''cpu'''				,     check_hash=lowercase				)
								else:
												__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)
				__lowercase					     =				checkpoint

				__lowercase					     =				create_rename_keys(lowercase				)
				for rename_key_src, rename_key_dest in rename_keys:
								rename_key(lowercase				,     lowercase				,     lowercase				)

				# load HuggingFace model
				__lowercase					     =				SwiftFormerForImageClassification(lowercase				).eval()
				hf_model.load_state_dict(lowercase				)

				# prepare test inputs
				__lowercase					     =				prepare_img()
				__lowercase					     =				ViTImageProcessor.from_pretrained('''preprocessor_config'''				)
				__lowercase					     =				processor(images=lowercase				,     return_tensors='''pt'''				)

				# compare outputs from both models
				__lowercase					     =				get_expected_output(lowercase				)
				__lowercase					     =				hf_model(inputs['''pixel_values''']				).logits

				assert hf_logits.shape == torch.Size([1, 1000]				)
				assert torch.allclose(hf_logits[0, 0:5]				,     lowercase				,     atol=1E-3				)

				Path(lowercase				).mkdir(exist_ok=lowercase				)
				print(F"Saving model {swiftformer_name} to {pytorch_dump_folder_path}"				)
				hf_model.save_pretrained(lowercase				)


if __name__ == "__main__":
		__a    :	Optional[int]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--swiftformer_name""",
		    default="""swiftformer_xs""",
		    choices=["""swiftformer_xs""", """swiftformer_s""", """swiftformer_l1""", """swiftformer_l3"""],
		    type=str,
		    help="""Name of the SwiftFormer model you'd like to convert.""",
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""",
		    default="""./converted_outputs/""",
		    type=str,
		    help="""Path to the output PyTorch model directory.""",
		)
		parser.add_argument("""--original_ckpt""", default=None, type=str, help="""Path to the original model checkpoint.""")

		__a    :	Optional[int]         =    parser.parse_args()
		convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)